Corona Detection Using Audio Data

ABSTRACT

Systems, methods, and apparatus for corona detection using audio data are provided. In one example embodiment, the method includes obtaining, by one or more computing devices, audio data indicative of audio associated with an electrical system for at least one time interval. The method includes partitioning, by the one or more computing devices, the audio data for the time interval into a plurality of time windows. The method includes determining, by the one or more computing devices, a signal indicative of a presence of corona based at least in part on audio data collected within an identified time window of the plurality of time windows relative to audio data collected for a remainder of the time interval.

PRIORITY CLAIM

The present application claims the benefit of priority of U.S.Provisional Patent Application Ser. No. 62/560,359, filed on Sep. 19,2017, titled “Corona Detection Using Audio Data,” which is incorporatedherein by reference. The present application claims the benefit ofpriority of U.S. Provisional Patent Application Ser. No. 62/544,164,filed Aug. 11, 2017, titled “Corona Detection Using Audio Data,” whichis incorporated herein by reference.

FIELD

The present disclosure relates generally to detection of corona usingaudio data.

BACKGROUND

Corona is a type of electric discharge occurring in electrical systemsin areas of very high electric field strength. Corona can becharacterized by a glow, electromagnetic emanation, and/or a sound oftendescribed as sizzling bacon. For instance, when corona is visible, itcan have a blue glow and can have significant brightness in ultravioletwavelengths. Corona can cause damage to, for instance, wires,insulators, and/or other components of an electrical system.

Techniques for detecting corona can include ultraviolet detection,ultrasonic detection, and/or RF emission detection. However, suchtechniques can suffer disadvantages. For example, power lines cangenerate corona that can be detected using images captured by camerasoperating in the ultraviolet spectrum. Such cameras, however, can belarge and expensive. In some cases, the detection methods can require auser to manually operate a device and aim at an area suspected tocontain corona. As such the detection methods can be cumbersome andnon-autonomous.

SUMMARY

Aspects and advantages of embodiments of the present disclosure will beset forth in part in the following description, or may be learned fromthe description, or may be learned through practice of the embodiments.

One example aspect of the present disclosure is directed to a method fordetecting corona in an electrical system. The method can includeobtaining, by one or more computing devices, audio data indicative ofaudio associated with the electrical system for at least one timeinterval. The method can include partitioning, by the one or morecomputing devices, the audio data for the time interval into a pluralityof time windows. The method can include determining, by the one or morecomputing devices, a signal indicative of a presence of corona based atleast in part on audio data collected within an identified time windowof the plurality of time windows relative to audio data collected for aremainder of the time interval.

Other examples aspects of the present disclosure are directed toapparatus, electronic devices, non-transitory computer-readable media,smart clamps, power devices, and other devices configured to detectcorona in electrical systems based at least in part on audio data.

These and other features, aspects and advantages of various embodimentswill become better understood with reference to the followingdescription and appended claims. The accompanying drawings, which areincorporated in and constitute a part of this specification, illustrateembodiments of the present disclosure and, together with thedescription, serve to explain the related principles.

BRIEF DESCRIPTION OF THE DRAWINGS

Detailed discussion of embodiments directed to one of ordinary skill inthe art are set forth in the specification, which makes reference to theappended figures, in which:

FIG. 1 depicts a block diagram of example processing of audio data forcorona detection according to example embodiments of the presentdisclosure;

FIG. 2 depicts example audio data for a time interval partitioned into aplurality of time windows according to example embodiments of thepresent disclosure;

FIG. 3 depicts an example scaling function for scaling ratios based ontotal energy of audio data for a time interval according to exampleembodiments of the present disclosure;

FIG. 4 depicts an example computation of a confidence score indicativeof the presence of corona based at least in part on scaled ratiosaccording to example embodiments of the present disclosure;

FIG. 5 depicts an example computation of a confidence score indicativeof the presence of corona based at least in part on scaled ratiosaccording to example embodiments of the present disclosure;

FIG. 6 depicts an example transmission tower supporting transmissionlines connected via suspension clamps;

FIG. 7 depicts a perspective view of a suspension clamp;

FIG. 8 depicts a cross section view of a suspension clamp;

FIG. 9 depicts a perspective view of a portion of a suspension clamp;

FIG. 10 depicts a perspective view of an example clamp configured to besecured to a conductor away from a transmission tower according toexample embodiments of the present disclosure;

FIG. 11 depicts a block diagram of an example system according toexample embodiments of the present disclosure;

FIG. 12 depicts a flow diagram of an example method according to exampleembodiments of the present disclosure;

FIG. 13 depicts a flow diagram of an example method according to exampleembodiments of the present disclosure; and

FIG. 14 depicts a flow diagram of an example method according to exampleembodiments of the present disclosure.

DETAILED DESCRIPTION

Reference now will be made in detail to embodiments, one or moreexamples of which are illustrated in the drawings. Each example isprovided by way of explanation of the embodiments, not limitation of thepresent disclosure. In fact, it will be apparent to those skilled in theart that various modifications and variations can be made to theembodiments without departing from the scope or spirit of the presentdisclosure. For instance, features illustrated or described as part ofone embodiment can be used with another embodiment to yield a stillfurther embodiment. Thus, it is intended that aspects of the presentdisclosure cover such modifications and variations.

Example aspects of the present disclosure are directed to coronadetection using audio data. Certain technology for identifying coronacan involve detection of energy at a primary frequency (e.g., 60 Hz, 50Hz, etc.) for alternating current in an electrical system with someharmonics of the primary frequency. However, in some cases, there can belittle energy in the audio band at the primary frequency. A pulse ormultiple pulses of high frequency can occur approximately at the peakvoltage, but the pulse position can move substantially relative to thepeak. This can make locating the peak for corona detection difficult,particularly in the presence of a 60 Hz or 50 Hz hum created by, forinstance, transformers or power lines in the power system. Moreover, thepulse shape associated with corona can be chaotic. The pulse can evendisappear for several consecutive cycles during a high level coronaevent, making corona detection difficult.

According to example aspects of the present disclosure, audio dataobtained, for instance, using a microphone is used to identify coronadischarges in an electrical system (e.g., such as power transmissionlines). More particularly, energy for the audio data can be obtained fora time interval that is selected, for instance, based at least in parton the primary frequency of the electrical system (e.g., 60 Hz or 50Hz), such as a sensed primary frequency as determined by one or morefeedback devices. The audio data for the time interval can bepartitioned into a plurality of time windows that are a fraction of thetime interval (e.g. time windows are 10% of the time interval). Energyassociated with the audio data can be determined for each time windowand for a time period outside the time window but within the timeinterval. A ratio of the energy values can be determined and used toprovide a signal indicative of the presence of corona.

The systems and methods of the present disclosure can be used to detectcorona in a variety of applications. For instance, in some embodiments,a device attached to a power line, such as a clamp, can be used as anattachment point for the corona sensor. In other cases, the coronasensor can be attached to the power line or proximate to the power line.The corona sensor can include or be in communication with a microphonefor obtaining audio data and/or can otherwise have access to audio data.The corona sensor can include one or more computing devices (e.g.,processors and/or memory devices) that implement corona detection logicused to detect the presence of corona by processing the audio dataaccording to example embodiments of the present disclosure. In someembodiments, the corona sensor can include communication capability andcan communicate (e.g., wirelessly transmit) information (e.g., coronadetection information) from the suspension clamp.

For instance, in some embodiments, a control action can be taken inresponse to the presence of corona. The control action can includeproviding a notification indicative of the presence of corona. Forinstance, the notification can be provided over a communicationinterface (e.g., over a network). In response to the notification,various measures can be taken in response to the presence of corona.

For instance, in some embodiments, upon the detection of coronaaccording to example embodiments of the present disclosure, anotification can be communicated over a network. The notification canprovide information associated with a corona event (e.g., timing and/orlocation of the corona event). The notification can be communicated inany suitable format (e.g., binary code, email, text message,communication signal, telephone communication, etc.)

In some embodiments, the notification can be provided to a power controlsystem. The power control system can process the information and takecorrective action based on the notification. For instance, the powercontrol system can temporarily adjust the power over a transmission linein response to the corona event. The power control system can send analert signal ordering a maintenance action to address the corona event.

Aspects of the present disclosure are discussed with reference toimplementing corona detection in a clamp for purposes of illustrationand discussion. Those of ordinary skill in the art, using thedisclosures provided herein, will understand that the systems andmethods disclosed herein can be used in a variety of other applications.For example, the systems and methods of the present disclosure can beimplemented using hand held devices; drones; directional microphones;user devices (e.g., smartphones, tablets, laptops, etc.); in utilitycabinets; in vehicles (e.g., aerial vehicles and/or ground basedvehicles); as part of ground fault interrupter systems, and/or in otherapplications.

Aspects of the present disclosure can provide a number of technicaleffects and benefits. For example, by using audio signals, coronadetection can be implemented using signals obtained with less expensiveequipment, such as microphones. Moreover, corona detection according toexample embodiments can look for pulsed energy in the audio band thatrepeats at approximately the line frequency of the electrical system.This can allow for a substantial tolerance for pulse variation whilestill reliably providing a signal for detection of corona.

In addition, aspects of present disclosure can provide an improvement tocomputing technology, such as computing technology implemented in, forinstance, a clamp. For example, the systems and methods according toexample aspects of the present disclosure can provide more reliableprocessing of audio signals to identify corona, leading to fewer falsepositives and more efficient use of computing resources (e.g.,processors, memory devices, etc.). The computing resources can bepreserved for more core functions, such as recordation and storage ofother data associated with an electrical system, communicating data viaa network interface, etc. In addition, communication of more reliabledata and fewer false positives can reduce communication latenciesresulting from communicating unnecessary or unreliable data over anetwork.

With reference now to the FIGS., example embodiments of the presentdisclosure will now be set forth. FIG. 1 depicts a block diagram ofexample processing logic 100 for corona detection using audio dataaccording to example embodiments of the present disclosure. A microphone102 can collect audio signals. The microphone 102 can be any deviceconfigured to capture audio data. The audio data can be associated withthe portion of the audible frequency range at which typical human beingscan hear. For instance, the audio data can be associated with theaudible frequency range of about 20 Hz to about 20,000 Hz. The use ofthe term “about” in conjunction with a numerical value is intended torefer to within 20% of the stated amount.

The audio data collected by the microphone can be sampled at analog todigital conversion block 104 to convert the audio data from an analogsignal to a digital signal. Any suitable sample rate can be used in thepresent disclosure. For instance, in one example implementation, thesample rate can be about 44,100 samples per second. Other sample ratescan be used, such as 48,000 samples per second, 96,000 samples persecond, 32,000 samples per second or other sample rate. The samplefrequency may or may not be locked to the primary frequency of theelectrical system. At block 106, the processing logic takes the absolutevalue of the audio data.

The audio data can be collected for one or more time intervals. The timeinterval is determined based on the primary frequency of the electricalsystem. For instance, in a 60 Hz system, the time interval can be about16.67 ms. In a 50 Hz system, the time interval can be about 20 ms. Insome embodiments, the time interval can be determined based at least inpart on a sensed primary frequency of the electrical system.

The time interval can be partitioned into a plurality of time windows.For instance, in example implementations, each time window can have aduration of about 10% of a duration of the time interval. As shown, inFIG. 1, each time window can be associated with a computation gate108.1, 108.2, . . . 108.10. FIG. 1 contemplates ten computation gates108.1, 108.2, . . . 108.10. However, more or fewer computation gates canbe used without deviating from the scope of the present disclosure.

Each computation gate includes a first low pass filter (“LPW Window”)and a second low pass filter (“LPF Noise”). The first filter can beconfigured to determine a first energy associated with audio datacollected during the time window. The second filter can be configured todetermine a second energy associated with the remainder of the timeinterval.

In some embodiments, the first filter can be a single pole low passfilter. The first filter can be associated with a transfer function:

y[n]=α _(c*) y[n−1]+(1−α_(c))*x[n]

where n is the sample point, y is the output of the transfer function, xis the absolute value of the audio data, and α_(c) is a constant. Thevalue of α_(c) can be selected to provide a compromise between fastresponse and good average. In some embodiments, the value of α_(c) canbe in the range of 0.95 to 0.9999999, such as in the range of about 0.98to about 0.9999999, such as in the range of about 0.99 to about0.9999999.

In some embodiments, the second filter can be a single pole low passfilter. The first filter can be associated with a transfer function:

y[n]=α _(n*) y[n−1]+(1−α_(n))*x[n]

where n is the sample point, y is the output of the transfer function, xis the absolute value of the audio data, and α_(c) is a constant. Thevalue of α_(n) can be selected to provide a compromise between fastresponse and good average. In some embodiments, the value of α_(n) canbe in the range of 0.95 to 0.9999999, such as in the range of about 0.98to about 0.9999999, such as in the range of about 0.99 to about0.9999999. The value of α_(c) and α_(n) can be selected such that thestep response of the first filter and the second filter is the samewithin the time interval. Other suitable measurements of the firstenergy and second energy can be used without deviating from the scope ofthe present disclosure.

FIG. 2 depicts a graphical representation 120 of partitioning audio datafor a time interval into a plurality of time windows according toexample embodiments of the present disclosure. As shown, the audio data122 includes a portion 50 associated with a corona event. The audio datais partitioned into ten time windows 124. Each time window 124 isassociated with a duration of about 10% of a duration of the timeinterval. Each computation gate 108.1, 108. 2, . . . 108.10 of FIG. 1determines an energy associated with audio data within the time window124 as well as energy associated with audio data in the remainder 126(e.g., noise) of the time interval.

Referring to FIG. 1, at stage 105, the processing logic 100 determinesratios Q of the first energy associated with the audio data within atime window to the second energy associated with the remainder of thetime interval. If random noise is present, the output of the firstfilter and the second filter for each computation gate 108.1, 108.2, . .. 108.n will be about the same. If corona exists within a time window,the first energy associated with audio data within the time window willbe higher than the second energy associated with audio data within theremainder of the time interval. In this way, the ratio of the firstenergy to the second energy for each time window can be indicative ofthe presence of corona within the time window.

Referring to FIG. 1, a sound level computation 110 is always activeduring a time interval. The sound level computation 110 determines atotal energy E for the time interval. The total energy E can be used todetermine an adequacy of the sound level to detect corona and to whatdegree. If the total energy E is less than a first threshold, coronadetection may not be permitted. If the total energy E is between a firstthreshold and a second threshold, corona detection can be linearlyscaled as discussed below. If the total energy E is above the secondthreshold, full corona detection based on the ratios Q can be permitted.

More particularly, a signal indicative of total energy E can be providedto function 112 to generate scaling factor p. Scaling factor p can beused for scaling ratios Q to scaled ratios based on the adequacy ofaudio data to detect corona for the time interval.

FIG. 3 depicts a graphical representation of one example function 112for generating scaling factor p based at least in part on the totalenergy E for the time interval. FIG. 3 plots total energy E along thehorizontal axis and scaling factor p along the vertical axis. As shownby portion 112.1 of function 112, the scaling factor p can be 0 when thetotal energy E is less than a first threshold E₁. This can disablecorona detection when the audio signal is too low. As shown by portion112.2 of function 112, the scaling factor p can vary linearly when thetotal energy is greater than the first threshold E₁ but less than asecond threshold E₂. This can cause for linear scaling of the coronadetection at intermediate sound levels. While linear scaling is shown inFIG. 3, other suitable scaling regimes (e.g., exponential, sigmoid,hyperbolic tangent, non-linear, step function, etc.) can be used withoutdeviating from the scope of the present disclosure. As shown by portion112.3 of function 112, the scaling factor p can be 1 when the totalenergy is greater than the second threshold E₂ to allow for full coronadetection.

Referring to FIG. 1 at stage 115, the processing logic 100 can determinea scaled ratio R for each time window using a function that determinesthe scaled ratio R based at least in part on the ratio Q for the timewindow and the scaling factor p. In some example embodiments, thefunction implemented at stage 115 can be:

R=p*(Q−1)+1

This function scales the value of the scaled ratio R toward 1 when thetotal signal level of the audio data is low. Other suitable functionscan be used without deviating from the scope of the present disclosure.

In some instances, a narrow corona may span two adjacent time windowswithin a time interval due to the misalignment of the corona event tothe time windows. Example aspects of the present disclosure can processthe signals based on ratios in adjacent time windows to improvedetection of corona that can span several time windows. Moreparticularly, the scaled ratios R can be provided to confidencecomputation gate 130 to determine a confidence score C indicative of thepresent of corona based at least in part on scaled ratios of adjacenttime windows and/or time windows that are opposing in phase.

More particularly, computation gate 130 can identify a time window witha maximum scaled ratio of the plurality of time windows (“maximum timewindow”). The computation gate 130 can examine the scaled ratios of thetime windows adjacent to the maximum time window, both to the left andto the right. Any value of the scaled ratio above 1 for an adjacent timewindow can be added to the scaled ratio for the maximum time window.

In addition, a time window opposing in phase (e.g., 180° out of phase)(“opposing phase time window”) with the maximum time window can beidentified. Time windows adjacent to the opposing phase time window canalso be examined. An example implementation is provided below:

1. Find the largest R[n] and record the peak index as ‘m’

2. C=R[n]

3. if R[m−1]>1.0 then C=C+(R[m−1]−1)4. if R[m+1]>1.0 then C=C+(R[m+1]−1)5. m=m+5//point to bin opposite in phase6. if R[m]>1.0 then C=C−(R[m]−1)7. if R[m-1]>1.0 then C=C+(R[m−1]−1)8. if R[m+1]>1.0 then C=C+(R[m+1]−1)

FIG. 4 depicts an example computation of a confidence score bycomputation gate 130 according to example embodiments of the presentdisclosure. FIG. 4 plots scaled ratios R for each of time windows 0-9.As shown, time window 2 is associated with a maximum scaled ratio of1.3. Adjacent time window 1 has a scaled ratio of 1.1. 0.1 will be addedto the scaled ratio of 1.3 to obtain 1.4. Adjacent time window 3 has ascaled ratio of 0.9. No adjustments are made based on this adjacent timewindow. Time window 7 is opposite in phase to time window 3. Time window7 has a value 1.05. In that regard, 0.05 is subtracted from 1.4 toobtain a confidence score C of 1.35.

As shown in FIG. 1, the confidence score C can be provided to a low passfilter 135 to determine a signal S indicative of the presence of corona.The low pass filter 135 can be used to smooth over false coronaindications. In some embodiments, the confidence score C is presentedonce per time interval to the low pass filter 135. The low pass filtercan be a single pole low pass filter associated with a transferfunction:

S[n]=α _(t*) S[n−1]+(1−α_(t))*C[n]

where n is the sample point, S is the output of the transfer function, Cis the confidence score, and α_(t) is a constant. The value of α_(t) canbe selected to provide a faster response with reduced false triggers.The value of α_(t) can be in the range of, for instance 0.95 to0.9999999, such as in the range of about 0.98 to about 0.9999999, suchas in the range of about 0.99 to about 0.9999999. The value of α_(t) canbe selected to provide a faster response with reduced false triggers.

The signal S can be indicative of the likelihood of corona within a timeinterval. For instance, in some implementations, if S is less than 1.2,there is probably no corona. If S is between 1.2 and 1.25, there isprobable corona. If S is between 1.25 and 1.4 there is likely corona. IfS is greater than 1.4 there is a very high likelihood of corona.

The above signal processing method is just one example of determinationof a signal indicative of a presence of corona according to exampleembodiments of the present disclosure. Variations and modifications canbe made to this example method without deviating from the scope of thepresent disclosure. For instance, in another example, the followingfunction can be used to determine the scale ratio:

R=p*(Q−1)

This function scales the value of the scaled ratio R towards 0 when thetotal signal level of the audio data is low. The value of R can be belowzero.

As discussed above, the scaled ratios R can be provided to confidencecomputation gate 130 to determine a confidence score C indicative of thepresence of corona based at least in part on scaled ratios of adjacenttime windows and/or time windows that are opposing in phase.

More particularly, in this example, computation gate 130 can identify atime window with a maximum scaled ratio of the plurality of time windows(“maximum time window”). The computation gate 130 can examine the scaledratios of the time windows adjacent to the maximum time window, both tothe left and to the right. Any value of the scaled ratio above 0 for anadjacent time window can be added to the scaled ratio for the maximumtime window.

In addition, a time window opposing in phase (e.g., 180° out of phase)(“opposing phase time window”) with the maximum time window can beidentified. Time windows adjacent to the opposing phase time window canalso be examined. An example implementation is provided below:

1. Find the largest R[n] and record the peak index as ‘m’

2. C=R[n]

3. if R[m−1]>0.0 then C=C+(R[m−1])4. if R[m+1]>0.0 then C=C+(R[m+1])5. m=m+5//point to bin opposite in phase6. if R[m]>0.0 then C=C−(R[m])7. if R[m−1]>0.0 then C=C+(R[m−1])8. if R[m+1]>0.0 then C=C+(R[m+1])

FIG. 5 depicts an example computation of a confidence score bycomputation gate 130 according to example embodiments of the presentdisclosure. FIG. 5 plots scaled ratios R for each of time windows 0-9.As shown, time window 2 is associated with a maximum scaled ratio of0.3. Adjacent time window 1 has a scaled ratio of 0.1. 0.1 will be addedto the scaled ratio of 0.3 to obtain 0.4. Adjacent time window 3 has ascaled ratio of −0.1. No adjustments are made based on this adjacenttime window. Time window 7 is opposite in phase to time window 3. Timewindow 7 has a value 0.05. In that regard, 0.05 is subtracted from 0.4to obtain a confidence score C of 0.35.

The confidence score C can be provided to a low pass filter 135 todetermine a signal S indicative of the presence of corona. The low passfilter 135 can be used to smooth over false corona indications. In someembodiments, the confidence score C is presented once per time intervalto the low pass filter 135. The low pass filter can be a single pole lowpass filter associated with a transfer function:

S[n]=α _(t*) S[n−1]+(1−α_(t))*C[n]

where n is the sample point, S is the output of the transfer function, Cis the confidence score, and α_(t) is a constant. The value of α_(t) canbe selected to provide a faster response with reduced false triggers.The value of α_(t) can be in the range of, for instance, 0.95 to0.9999999, such as in the range of about 0.98 to about 0.9999999, suchas in the range of about 0.99 to about 0.9999999. The value of a can beselected to provide a faster response with reduced false triggers.

The signal S can be indicative of the likelihood of corona within a timeinterval. For instance, in some implementations, if S is less than 0.2,there is probably no corona. If S is between 0.2 and 0.25, there isprobable corona. If S is between 0.25 and 0.4 there is likely corona. IfS is greater than 0.4 there is a very high likelihood of corona.

One example application of the present disclosure is for use with aclamp (e.g., a suspension clamp) used in electrical systems. FIG. 6shows a transmission tower 200 which is used to suspend powertransmission lines 202 above the ground. The tower 200 has cantileveredarms 204. Insulators 206 extend down from the arms 204. One or moresuspension clamps 208 are located at the bottom ends of the insulators206. The lines 202 are connected to the suspension clamps. Thesuspension clamps 208 hold the power transmission lines 202 onto theinsulator 206.

FIGS. 7-9 illustrate an example of a suspension clamp 208, whichgenerally includes an upper section 210 and a lower support section 212.These two sections 210, 212 each contain a body 214, 216 which form asuspension case. The bodies 214, 216 each comprise a longitudinal trough(or conductor receiving area) 215, 217 that allow the transmissionconductor 202 to be securely seated within the two sections when the twosections are bolted (or fastened) together by threaded fasteners 201(not shown). This encases the transmission conductor 202 between the twobodies to securely contain the transmission conductor 202 on the clamp208. Threaded fasteners are not required and any other suitablefastening configuration may be provided. The two bodies 214, 216connected together are suspended via a metal bracket 218 that attachesto the lower body 216 at points via bolt hardware 220.

The lower body, or lower body section, 216 includes a first end 219 anda second end 221. The conductor receiving area (or conductor contactsurface) 217 extends from the first end 219 to the second end 221 alonga top side of the lower body 216. The conductor receiving area,including longitudinal trough 217, forms a lower groove portion forcontacting a lower half of the conductor 202. A general groove shape isnot required, and any suitable configuration may be provided.

In one implementation, the upper and lower sections 210, 212 each haveembedded within their respective bodies 214, 216 one-half of a currenttransformer 222, 224 that is commonly referred to in the industry as asplit core current transformer. When these components 222, 224 arejoined, they form an electromagnetic circuit that allows, in someapplications, the sensing of current passing through the conductor 202.In one implementation, the current transformer is used for currentsensing, data collection, data analysis and data formatting devices. Insome implementations the current transformer may be located outside ofthe clamp or similar device or, in some implementations, current may beprovided by another means.

The body 214 of the upper section 210 contains a first member 232 and asecond member 234 forming a cover plate. The first member 232 comprisesa first end 233, a second end 235, and a middle section 237 between thefirst end 233 and the second end 235. The conductor receiving area (orconductor contact surface) 215 extends from the first end 233 to thesecond end 235 along a bottom side of the first member 232. Theconductor receiving area 215 forms an upper groove portion forcontacting an upper half of the conductor 202. A general groove shape isnot required, and any suitable configuration may be provided. In oneimplementation, the first member 232 further comprises a recessed cavity226 at the middle section 237 that effectively contains an electroniccircuit 228. In this implementation, the electronic circuit 228 isdesigned to accept inputs from several sensing components. This cavity226 may be surrounded by a Faraday cage 230 to effectively nullify theeffects of high voltage EMF influence from the conductor 202 on thecircuitry 228. The Faraday cage may also surround the currenttransformer 222. The cover plate, or cover plate member, 234 can coverthe top opening to the cavity 226 to retain the electronic circuitinside the body, or upper body section, 214. The electronics may behoused in a metal or plastic container, surrounded by the noted Faradaycage, and the entire assembly can be potted, such as with epoxy forexample.

The electronic circuit 228 can accept and quantify in a meaningfulmanner various inputs for monitoring various parameters of the conductor202 and the surrounding environment. The inputs can also be derived fromexternally mounted electronic referencing devices/components. The inputscan include, for example: Line Current reference (as derived from theCurrent transformer 222, 224 or other means); Barometric pressure andTemperature references—internal and ambient (as derived from internaland external thermocouples 236, 238 or other means); Vibrationreferences of the conductor (as derived from the accelerometer 240, suchas a 0.1-128 Hz sensor, for example, or other means); and Opticalreferences (as derived from the photo transistor 242 in a fiber optictube or other means). Tensile references from the tensile indicators 244may, for example, provide information indicating that ice is forming asthe weight of the conductor increases due to ice build up.

Supervisory Control And Data Acquisition (SCADA) generally refers to anindustrial control system such as a computer system monitoring andcontrolling a process. Information derived by the electrical/electroniccircuitry can exit the circuit 228 via a non-conductive fiber opticcable 246 and be provided up and over to the transmission tower 200 andultimately at the base of the tower and fed into the user's SCADA systemto allow the end user to access and view electrical and environmentalconditions at that sight, or the information can be transmitted to aremote or central site. The clamp or other sensing device may bealternatively configured to wirelessly transmit information from theelectronic circuit 228 to a receiver system. Collected data can also beprovided to customer via communication and data collections systems thatare not part of the SCADA system.

According to example embodiments of the present disclosure, electroniccircuitry 228 can include one or more computing devices configured toimplement logic for corona detection according to example embodiments ofthe present disclosure. The electronic circuitry 228 can include amicrophone and one or more computing devices. In some embodiments, theelectronic circuitry 228 can implement the system for detecting coronadiscussed in detail with reference to FIG. 11 below.

In some embodiments, the one or more computing devices can be locatedremotely from the clamp. For instance, the electronic circuitry 228 cancommunicate audio data collected by a microphone to a remote computingsystem (e.g., a server computing system, smartphone, table, etc.). Theaudio data can be processed by the remote computing system according toexample aspects of the present disclosure for corona detection.

In some embodiments, the systems and methods for corona detectionaccording to aspects of the present disclosure can be implemented in aclamp configured to be secured to a power transmission line away from asupport tower. FIG. 10 depicts an example clamp 260 configured to besecured to a power transmission line or other conductor away from asupport tower and configured for of corona detection according toexample aspects of the present disclosure. As shown the clamp 260 caninclude upper and lower sections 262, 264 configured to be secured to apower transmission line. Upper and lower sections 262 and 264 can eachhave embedded within their respective bodies one-half of a currenttransformer. When these components 262 and 264 are joined, they form anelectromagnetic circuit that allows, in some applications, the sensingof current passing through the conductor. In one implementation, thecurrent transformer is used for current sensing, data collection, dataanalysis and data formatting devices.

In some embodiments, the clamp can include a housing 265. The housing265 can house an electronic circuit 270. The electronic circuit 270 canaccept and quantify in a meaningful manner various inputs for monitoringvarious parameters of the conductor and the surrounding environment. Theinputs can also be derived from externally mounted electronicreferencing devices/components. The inputs can include, for example:Line Current reference (as derived from the current transformer or othermeans); Barometric pressure and Temperature references—internal andambient (as derived from internal and external thermocouples or othermeans); Vibration references of the conductor (as derived from theaccelerometer, such as a 0.1-128 Hz sensor, for example, or othermeans); and Optical references (as derived from the photo transistor ina fiber optic tube or other means). Tensile references from the tensileindicators may, for example, provide information indicating that ice isforming as the weight of the conductor increases due to ice buildup.

The clamp may be alternatively configured to transmit information (e.g.,through a combination of wired and/or wireless links) from theelectronic circuit 270 to a receiver system. Collected data can also beprovided to the customer via communication and data collections systemsthat are not part of the SCADA system.

According to example embodiments of the present disclosure, electroniccircuitry 270 can include one or more computing devices configured toimplement logic for corona detection according to example embodiments ofthe present disclosure. The electronic circuitry 270 can include amicrophone and one or more computing devices. In some embodiments, theelectronic circuitry 270 can implement the system for detecting coronadiscussed in detail with reference to FIG. 11 below.

In some embodiments, the one or more computing devices can be locatedremotely from the clamp. For instance, the electronic circuitry 270 cancommunicate audio data collected by a microphone to a remote computingsystem (e.g., a server computing system, smartphone, table, etc.). Theaudio data can be processed by the remote computing system according toexample aspects of the present disclosure for corona detection.

Aspects of the present disclosure are discussed with reference to aclamp configured to detect corona based at least in part on audio data.Those of ordinary skill in the art, using the disclosures providedherein, will understand that the systems and methods for coronadetection can be used in other applications.

As an example, the systems and methods for corona detection based onaudio data can be implemented using a drone or other aerial vehicle. Thedrone or other aerial vehicle can include a microphone (e.g., adirectional microphone). The drone or other aerial vehicle can becontrolled to fly close to an electrical system (e.g., powertransmission lines, substations, etc.). Audio data picked up by themicrophone can be processed according to example embodiments of thepresent disclosure for corona detection.

As another example, the system and methods for corona detection based onaudio data can be implemented using a ground based vehicle. The groundbased vehicle can include a microphone (e.g., a directional microphone).The ground based vehicle can be driven under or near an electricalsystem (e.g., power transmission lines, substations, etc.). Audio datapicked up by the microphone can be processed according to exampleembodiments of the present disclosure for corona detection.

As another example, the systems and methods for corona detection basedon audio data can be implemented in a utility cabinet housing variouscomponents of an electrical system (e.g., switches, transformers, buses,conductors, etc.). A microphone and one or more computing devices (e.g.,processors and one or more memory devices) can be located within thecabinet. Audio data collected by the microphone can be processedaccording to example embodiments of the present disclosure for coronadetection.

As another example, the systems and methods for corona detection basedon audio data can be implemented in a user device, such as a smartphone,tablet, wearable device, laptop, or device capable of being carried by auser while in operation. The user device can include a microphoneconfigured to capture audio data. Audio data picked up by the microphonecan be processed (e.g., either locally on the device or remotely)according to example embodiments of the present disclosure for coronadetection.

FIG. 11 depicts a block diagram of an example system 300 according toexample embodiments of the present disclosure. As discussed above, thesystem 300 can be implemented as part of a clamp or other device (e.g.,ground based or aerial vehicle). The system 300 includes a microphone302. The microphone 302 can be configured to capture audio data. In someembodiments, the microphone 302 can be a directional microphoneconfigured to capture audio data from a distance. The audio datacaptured by the microphone 302 can be provided to a computing system 310having one or more computing devices.

The computing system 310 can include one or more processors 312 and oneor more memory devices 314. The one or more processors 312 can be anysuitable processing device (e.g., a processor core, a microprocessor, anASIC, a FPGA, a controller, a microcontroller, etc.) and can be oneprocessor or a plurality of processors that are operatively connected.The memory devices 314 can include one or more non-transitorycomputer-readable storage media, such as RAM, ROM, EEPROM, EPROM, one ormore memory devices, flash memory devices, etc., and combinationsthereof.

The memory devices 314 can store instructions 316 and data 318accessible by the one or more processors 312. The instructions 316 canbe instructions used to implement logic for corona detection accordingto example embodiments of the present disclosure, such as the processinglogic depicted in FIG. 1 and/or the operations depicted in FIGS. 12-14.The instructions 316 can be programmed in software and/or hardwareimplemented. When implemented in software, any suitable programminglanguage can be used.

The system 300 can further include a network interface 320. The networkinterface 320 can be used to communicate information (e.g., informationassociated with detected corona) to other devices over a network (e.g.,via an optical fiber, via wireless communication, etc.). The networkinterface 320 can include one or more of, for example, a communicationscontroller, receiver, transceiver, transmitter, port, conductors,software and/or hardware for communicating data.

FIGS. 12-14 depict flow diagrams associated with an example method (400)according to example embodiments of the present disclosure. The methodcan be implemented, for instance, by the one or more computing devicesdepicted in FIG. 11. The method can implement the processing logicdiscussed in more detail with respect to FIG. 1. FIGS. 12-14 depictsteps performed in a particular order for purposes of illustration anddiscussion. Those of ordinary skill in the art, using the disclosuresprovided herein, will understand that various steps of any of themethods disclosed herein can be omitted, adapted, expanded, performedsimultaneously, rearranged, and/or modified in various ways withoutdeviating from the scope of the present disclosure.

At (410), the method can include obtaining audio data. The audio datacan be indicative of audio of an electrical system. The audio data canbe obtained from, for instance, a microphone. The audio data can beobtained for at least one time interval. The time interval can bedetermined based on a primary frequency of the electrical system (e.g.,60 Hz or 50 Hz). For instance, in a 60 Hz system, the time interval canbe about 16.67 ms). In a 50 Hz system, the time interval can be about 20ms.

At (420), the method can include partitioning the audio data into aplurality of time windows. Each time window can be a subpart of the timeinterval. In some embodiments, the audio data can be partitioned intoten time windows where each time window has a duration of about 10% of aduration of the time interval.

At (430), the method includes determining a scaled ratio for each timewindow. The scaled ratio for each time window can be used to determine asignal indicative of the presence of corona based on audio datacollected within an identified time window of the plurality of timewindows relative to audio data collected for the remainder of the timeinterval.

FIG. 13 depicts a flow diagram of determining a scaled ratio for eachtime window according to example embodiments of the present disclosure.At (432), the method includes determining a first energy for the timewindow based at least in part on audio data collected within the timewindow. The first energy can be a measure of the audio data collectedwithin the time window. The first energy can be determined, forinstance, using a low pass filter or other suitable methods.

At (434), the method includes determining a second energy for the timewindow based at least in part on audio data collected for the remainderof the time interval outside the time window. The second energy can be ameasure of the audio data collected within the time window. The secondenergy can be determined, for instance, using a low pass filter or othersuitable methods.

At (436), the method can include determining a ratio of the first energyand the second energy. For instance, the method can include dividing thefirst energy by the second energy to determine the ratio.

At (438), the method can include determining a scaling factor based atleast in part on a total energy for the time interval. The scalingfactor can be based on a total energy for the time interval. The scalingfactor can be an indicator of an adequacy of a sound level for the audiodata to detect corona during the time interval.

In example embodiments, as discussed with reference to FIGS. 1 and 3above, the scaling factor can be determined based on total energy forthe time interval. More particularly, the scaling factor can be 0 whenthe total energy is less than a first threshold. This can disable coronadetection when the audio signal is too low. The scaling factor can varylinearly when the total energy is greater than the first threshold butless than a second threshold. This can cause for linear scaling of thecorona detection at intermediate sound levels. The scaling factor can be1 when the total energy is greater than the second threshold to allowfor full corona detection.

At (440) of FIG. 12, the scaled ratio for the time window is determinedbased at least in part on the scaling factor and the ratio of the firstenergy and the second energy for the time window. In someimplementation, as discussed above, the scaled ratio can be determinedas follows:

R=p*(Q−1)

where p is the scaling factor, Q is the ratio of the first energy andthe second energy, and R is the scaled ratio.

Referring to FIG. 12 at (450), the method can include determining aconfidence score based at least in part on the scaled ratio. Theconfidence score can be indicative of the presence of corona in theelectrical system. Example aspects of the present disclosure can processthe signals based on ratios in adjacent time windows to improvedetection of corona that can span several time windows.

FIG. 14 depicts a flow diagram of determining a confidence scoreaccording to example embodiments of the present disclosure. At (452),the method includes identifying a maximum time window associated with amaximum scaled ratio. At (454), the method includes identifying adjacenttime windows to the maximum time window. At (456), an adjustment is madeto the scaled ratio for the maximum time window based on a scaled ratiofor the adjacent time windows. For example, any value of the scaledratio above 0 for an adjacent time window can be added to the scaledratio for the maximum time window.

At (458), the method can include identifying an opposing phase timewindow. The opposing phase time window can be, for instance, 180° out ofphase with the maximum time window. At (460), the method can includeadjusting the scaled ratio associated with the maximum time window todetermine the confidence score. For example, if the value of a scaledratio for opposing phase time window is greater than 0, the value of thescaled ratio above 0 can be subtracted from the scaled ratio associatedwith the maximum time window to generate the confidence score.

Referring to FIG. 12 at (470), the method can include determining asignal indicative of corona based at least in part on the confidencescore. For example, in some embodiments, the confidence score can beprovided to a low pass filter to determine a signal indicative of thepresence of corona.

In some embodiments, information can be added (e.g., time stamps) to thesignal indicative of the presence of corona to enrich the informationassociated with the signal indicative of the presence of corona. In someembodiments, the signal indicative of the presence of corona can becommunicated by the system via a network interface. For instance, thesignal can be communicated over a network to one or more other devices.

While the present subject matter has been described in detail withrespect to specific example embodiments thereof, it will be appreciatedthat those skilled in the art, upon attaining an understanding of theforegoing may readily produce alterations to, variations of, andequivalents to such embodiments. Accordingly, the scope of the presentdisclosure is by way of example rather than by way of limitation, andthe subject disclosure does not preclude inclusion of suchmodifications, variations and/or additions to the present subject matteras would be readily apparent to one of ordinary skill in the art.

What is claimed is:
 1. A method for detecting corona in an electricalsystem, the method comprising: obtaining, by one or more computingdevices, audio data indicative of audio associated with the electricalsystem for at least one time interval; partitioning, by the one or morecomputing devices, the audio data for the time interval into a pluralityof time windows; and determining, by the one or more computing devices,a signal indicative of a presence of corona based at least in part onaudio data collected within an identified time window of the pluralityof time windows relative to audio data collected for a remainder of thetime interval.
 2. The method of claim 1, wherein determining, by the oneor computing devices, a signal indicative of a presence of coronacomprises: determining, by the one or more computing devices, a firstenergy associated with the identified time window based at least in parton the audio data; determining, by the one or more computing devices, asecond energy associated with the remainder of the time interval basedat least in part on the audio data; determining, by the one or morecomputing devices, a ratio of the first energy and the second energy;and determining, by the one or more computing devices, a signalindicative of the presence of corona based at least in part on the ratioof the first energy and the second energy.
 3. The method of claim 1,wherein the method comprises: determining, by the one or more computingdevices, a total energy for the time interval based on the audio datafor the time interval; and determining, by the one or more computingdevices, an indicator of an adequacy of a sound level for the audio datato detect corona during the time interval based at least in part on thetotal energy.
 4. The method of claim 1, wherein determining, by the oneor more computing devices, a signal indicative of a presence of coronabased at least in part on audio data collected within the identifiedtime window relative to audio data collected for a remainder of the timeinterval comprises: determining, by the one or more computing devices,the signal indicative of the presence of corona based at least in parton the ratio for the identified time window and a ratio for one or moreadjacent time windows in the time interval.
 5. The method of claim 4,wherein determining, by the one or more computing devices, the signalindicative of the presence of corona based at least in part on the ratiofor the identified time window and a ratio for one or more adjacent timewindows in the time interval, comprises: determining, by the one or morecomputing devices, a scaled ratio for each of the plurality of timewindows; identifying, by the one or more computing devices, a timewindow with a maximum scaled ratio of the plurality of time windows;identifying, by the one or more computing devices, one or more adjacenttime windows to the time window associated with the maximum scaledratio; identifying, by the one or more computing devices, an opposingphase time window to the time window associated with the maximum scaledratio; and determining, by the one or more computing devices, aconfidence score based on the maximum scaled ratio, a scaled ratioassociated with one or more adjacent time windows, and a scaled ratiofor the opposing phase time window.
 6. The method of claim 5, whereinthe scaled ratio for each of the plurality of time windows is determinedbased at least in part on the adequacy of a sound level for the audiodata to detect corona during the time interval.
 7. The method of claim4, wherein determining, by the one or more computing devices, the signalindicative of the presence of corona based at least in part on the ratiofor the identified time window and a ratio for one or more adjacent timewindows in the time interval comprises: filtering the confidence scoreto generate the signal indicative of the presence of corona.
 8. Themethod of claim 1, wherein the time interval is determined based atleast in part on the frequency of electrical power in the electricalsystem.
 9. The method of claim 1, wherein each time window has aduration of about 10% of a duration of the time interval.
 10. The methodof claim 1, wherein the method is implemented at least in part by one ormore processors associated with a clamp device.
 11. The method of claim1, wherein the method comprises providing time stamp data in the signalassociated with the presence of corona.
 12. The method of claim 1,wherein the method comprises communicating, by the one or more computingdevices, a notification associated with the presence of corona via anetwork interface.
 13. A system for detecting corona in an electricalsystem, the system comprising: a microphone configured to obtain audiodata associated with the electrical system; a network interface; one ormore processors; and one or more memory devices, wherein the one or morememory devices store computer-readable instructions that when executedby the one or more processors cause the one or more processors toperform operations, the operations comprising: obtaining audio data fromthe microphone, the audio data indicative of audio associated with theelectrical system for at least one time interval; partitioning the audiodata for the time interval into a plurality of time windows; determininga signal indicative of a presence of corona based at least in part onaudio data collected within an identified time window of the pluralityof time windows relative to audio data collected for a remainder of thetime interval; and communicating the signal indicative of a presence ofcorona via the network interface.
 14. The system of claim 13, whereinthe operation of determining a signal indicative of a presence of coronacomprises: determining a first energy associated with the identifiedtime window based at least in part on the audio data; determining asecond energy associated with the remainder of the time interval basedat least in part on the audio data; determining a ratio of the firstenergy and the second energy; and determining a signal indicative of thepresence of corona based at least in part on the ratio of the firstenergy and the second energy.
 15. The system of claim 13, wherein theoperations comprise: determining a total energy for the time intervalbased on the audio data for the time interval; and determining anadequacy of a sound level for the audio data to detect corona during thetime interval based at least in part on the total energy.
 16. The systemof claim 14, wherein the operation of determining a signal indicative ofthe presence of corona based at least in part on the ratio of the firstenergy and the second energy, comprises: determining a scaled ratio foreach of the plurality of time windows; identifying a time window with amaximum scaled ratio of the plurality of time windows; identifying oneor more adjacent time windows to the time window associated with themaximum scaled ratio; identifying an opposing phase time window to thetime window associated with the maximum scaled ratio; and determining aconfidence score based on one the maximum scaled ratio, a scaled ratioassociated with one or more adjacent time windows, and a scaled ratiofor the opposing phase time window.
 17. The system of claim 13, whereinthe microphone is a directional microphone on an aerial or ground basedvehicle.
 18. One or more tangible, non-transitory computer-readablemedia storing computer-readable instructions that when executed by oneor more processors cause the one or more processors to performoperations, the operations comprising: obtaining audio data from themicrophone, the audio data indicative of audio associated with theelectrical system for at least one time interval; partitioning the audiodata for the time interval into a plurality of time windows; determininga scaled ratio for each of the plurality of time windows; identifying atime window with a maximum scaled ratio of the plurality of timewindows; identifying one or more adjacent time windows to the timewindow associated with the maximum scaled ratio; identifying an opposingphase time window to the time window associated with the maximum scaledratio; determining a confidence score based on one the maximum scaledratio, a scaled ratio associated with one or more adjacent time windows,and a scaled ratio for the opposing phase time window; and determining asignal indicative of the presence of corona based at least in part onthe confidence score.
 19. The one or more tangible, non-transitorycomputer-readable media of claim 18, wherein the operation ofdetermining a scaled ratio for each of the plurality of time windowscomprises, for each time window: determining a first energy for the timewindow based on the audio data; determining a second energy for the timewindow based on the audio data; determining a ratio of the first energyand the second energy determining a total energy for the time intervalbased on the audio data; determining the scaled ratio for the time widowbased at least in part on the ratio of the first energy and the secondenergy and the total energy for the time interval.
 20. The one or moretangible, non-transitory computer-readable media of claim 18, whereinthe computer-readable media form a part of a clamp device.